Alignment-Path Distillation from Non-streaming ASR-LLMs for Streaming Speech Recognition

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In this paper, we propose an alignment-path distillation framework for streaming automatic speech recognition (ASR) with large language models (LLMs). Interleaved streaming ASR-LLMs use forced alignments (FA) from alignment models, such as those trained with connectionist temporal classification (CTC), to construct speech-text training sequences. However, alignments obtained from a separate acoustic model may be inconsistent with those learned by LLM-based ASR. This motivates us to transfer alignment information from a non-streaming ASR-LLM to improve streaming recognition. Specifically, we extract monotonic alignment paths from a non-streaming teacher's soft text-audio attention and use them to construct interleaved training sequences. The framework also includes logit and hidden-state distillation to learn from the teacher's output distributions and internal representations. Experimental results show that, without logit or hidden-state distillation, training with teacher-derived alignment paths achieves a 5.2% relative error rate reduction compared with training using forced alignments. When both models use logit and hidden-state distillation, teacher-derived alignments yield a 3.9% relative error rate reduction, with similar mean emission latency but higher flicker. The complete framework achieves a 16.6% relative error rate reduction compared with training using forced alignments without logit or hidden-state distillation.